---
library_name: transformers
license: mit
base_model: AnonymousCS/populism_multilingual_roberta_base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- recall
- precision
model-index:
- name: populism_model81
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# populism_model81

This model is a fine-tuned version of [AnonymousCS/populism_multilingual_roberta_base](https://huggingface.co/AnonymousCS/populism_multilingual_roberta_base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2954
- Accuracy: 0.9270
- F1: 0.368
- Recall: 0.6765
- Precision: 0.2527

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Recall | Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| No log        | 1.0   | 34   | 0.4249          | 0.9686   | 0.0    | 0.0    | 0.0       |
| 0.4338        | 2.0   | 68   | 0.3313          | 0.9575   | 0.3784 | 0.4118 | 0.35      |
| 0.2741        | 3.0   | 102  | 0.3236          | 0.9482   | 0.3913 | 0.5294 | 0.3103    |
| 0.2741        | 4.0   | 136  | 0.2919          | 0.9298   | 0.3770 | 0.6765 | 0.2614    |
| 0.2314        | 5.0   | 170  | 0.2954          | 0.9270   | 0.368  | 0.6765 | 0.2527    |


### Framework versions

- Transformers 4.47.1
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0